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KM for e-commerce growth

This article appears in the issue October 2014, [Vol 23 Issue 9]
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E-commerce companies are depending on knowledge management systems for growth, customer acquisition and retention and to manage variable costs.

With better knowledge management tools, e-commerce companies can expand their businesses. Such was the case with The Grommet, a launch platform for new products. The company relies on engaged customers to further validate the products it includes in its online catalogs so the products succeed after launch.

“We’ve been using Pinterest from the very beginning. In fact, we were one of the very first brands to jump on the platform,” explains Tori Tait, the company’s senior community manager. “Our business is based on discovering new to market products, launching them into the marketplace and amplifying them to a large audience. A big part of this process is how our community lifts up our products and shares them with their own networks.”

While some of the benefits from the Pinterest use were fairly obvious, The Grommet didn’t have any data to show which Pinterest efforts were the most effective. So the company brought in Curalate to provide data to drive the company’s Pinterest-based marketing efforts.

“With the data we get from Curalate, we’re paying close attention to what images are driving business and sales,” Tait says. “This data influences our other marketing efforts, like deciding which images to use in an e-mail campaign or which images we use in banner ads.”

The Curalate data enables The Grommet to accurately determine what images are trending best. Tait adds, “Seeing older images rise to the top signals to us it’s a great time to resurface these images in things like e-mail, advertising and onsite merchandising.”

In July, the company saw a 30 percent increase in clicks from Pinterest than for the same period the year before. Additionally, pins from The Grommet site are producing 11 percent more repins, likes and comments than the year before. The number of followers is up 28 percent. Most importantly, the new and ongoing followers are doing more than just sharing pins—sales are up 42 percent from the previous year.

“We have kept our eye on other solutions that have surfaced, but none appear to be as robust as Curalate,” Tait says. “They have a reputation of showing us data we didn’t even know we wanted to see. This is priceless for a fast-growing business like ours.”

Tait hopes to use Curalate to implement changes for larger marketing initiatives so The Grommet can continue to deliver relevant marketing to its customers.

Better customer targeting

Controlling customer acquisition and retention costs is one of the top concerns for e-commerce companies. BikeBerry, a large online store for bicycles and accessory kits—most notably engines for motorized two-wheelers—saw that returning customers spent about 30 percent more than new customers, but was unsure how to correctly align its e-mail advertising efforts to effectively market to those customers while also continuing to attract new customers.

“We were spending a ton of money on customer acquisition and not enough on our existing customer base,” says Jack Lin, the company’s president. The company’s e-mail marketing efforts depended on mass e-mails sent out periodically, but always at the same time of the day, when it would typically be noticed by only a small percentage of the company’s target audience.

Just as important, the company wanted to avoid squandering larger discounts on customers who were likely to buy again, and instead use the bigger offer to lure new customers who needed larger discounts before they would commit to purchases. So a couple of years ago, BikeBerry worked with Retention Science to restructure the e-mail marketing campaign.

Retention Science used its Customer Profiling Engine to automatically create tailored retention campaigns for every BikeBerry customer and prospect, tailoring discount offers to each. Those customers and prospects who had a high propensity to buy, according to the Retention Science algorithms, received discounts of 0 to 5 percent. Those with a moderate propensity to buy received discounts of 10 to 15 percent, and those with little likelihood to buy received discounts of 20 to 25 percent. Additionally, Retention Science’s algorithms helped determine the most effective times to send e-mails to customers and prospects.

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